Zhong Jinzhi, Meng Yanjun, Liu Zehao, Zeng Fangui
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 0330024, China.
Shanxi Key Laboratory of Coal and Coal Measure Gas Geology, Taiyuan 0330024, China.
ACS Omega. 2022 Apr 19;7(17):15037-15047. doi: 10.1021/acsomega.2c00751. eCollection 2022 May 3.
High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM images, such as unlocated fringe regions, single-threshold segmentation, unclassified fuzzy superpixels, and tedious fringe pruning, an intelligent recognition method based on semantic segmentation, deep neural networks, fuzzy superpixels, and other algorithms is proposed. For unlocated fringe regions, the fringe regions are automatically localized with semantic segmentation. The whole semantic segmentation network adopts DeepLab V3+ based on ResNet to reduce unnecessary operations brought by non-fringe regions. For single-threshold segmentation of the image, the image is chunked before anything else. The genetic-optimized watershed algorithm is applied to divide the fringe base maps and non-fringe ones in order to avoid the distortion caused by different lights and shades of the image. For the fuzzy superpixels between the fringes and non-fringes, a similarity category judgment method based on neighboring pixels is proposed to solve the problem of unclassified fuzzy superpixels and to enrich and perfect the information of the lattice fringe base map. Eventually, as for lattice fringe overlap caused by coals piling together, a similarity judgment method based on the fringes' characteristics is proposed to remove the bur portion of the lattice fringes and improve the pruning rate. Combining the above theories, a visualization tool based on MATLAB App Designer is designed, and the above four steps can be completed by this app to accurately display the results of coal aromatic lattice fringe identification in HRTEM images. Comparison with the lattice fringes drawn by leading experts shows that the fringes interpreted by this method are reliable. This method facilitates the extraction of lattice fringes in HRTEM, which lays the foundation for the labeling of HRTEM images in a variety of deep learning algorithms and facilitates the direct observation of coal structures by researchers.
高分辨率透射电子显微镜（HRTEM）能够直接获取煤的晶格条纹和结构参数。针对目前HRTEM图像中晶格条纹提取存在的问题，如条纹区域定位不准、单阈值分割、未分类的模糊超像素以及繁琐的条纹修剪等，提出了一种基于语义分割、深度神经网络、模糊超像素等算法的智能识别方法。对于条纹区域定位不准的问题，利用语义分割自动定位条纹区域。整个语义分割网络采用基于ResNet的DeepLab V3+，以减少非条纹区域带来的不必要运算。对于图像的单阈值分割，首先对图像进行分块。应用遗传优化的分水岭算法划分条纹基图和非条纹基图，以避免图像不同明暗度造成的失真。对于条纹与非条纹之间的模糊超像素，提出一种基于相邻像素的相似性类别判断方法，解决未分类模糊超像素问题，丰富和完善晶格条纹基图信息。最终，针对煤堆积在一起导致的晶格条纹重叠问题，提出一种基于条纹特征的相似性判断方法，去除晶格条纹的冗余部分，提高修剪率。结合上述理论，设计了基于MATLAB App Designer的可视化工具，该应用程序可完成上述四个步骤，准确显示HRTEM图像中煤芳香晶格条纹识别结果。与权威专家绘制的晶格条纹对比表明，该方法解释的条纹可靠。该方法有助于HRTEM中晶格条纹的提取，为多种深度学习算法中HRTEM图像的标注奠定基础，便于研究人员直接观察煤的结构。
